In vitro study of gas embolism formation in microfluidic vascular networks
Notice bibliographique
Résumé
Vascular gas embolism is a potentially life-threatening medical condition caused by the presence of gas bubbles in the vascular network, leading to reduced blood flow and possible disruptions in the transport of oxygen to tissues and vital organs. Despite its significant implications for patient health, particularly in iatrogenic contexts, the origins and underlying mechanisms remain poorly understood. Since the onset of this condition is physical in nature, the purpose of this study is to investigate the genesis of gas embolism and its varied manifestations by employing a series of abiotic microfluidic devices designed to simulate the human vascular system with different hematological and anatomical configurations, subjected to pressure-related scenarios.The characteristics of bubble formation were first correlated with increasing levels of pressure applied to different geometries of artificial blood vessels, modeled by the first set of microfluidic platforms. Vascular channels with larger widths and ‘intact’ thin walls produced the greatest bubble volumes, especially at high pressures, while smaller channel widths exhibited earlier onset of bubble generation at lower pressure thresholds. The origins of gas embolism were directly related to the presence of excess gases by varying the storage conditions of these vascular systems on a chip and monitoring the impact of pressured gases in the local material network. A second set of microfluidic platforms was then introduced to determine patterns of bubble formation in the presence of local damages and critical areas in the vascular walls. As the integrity of the wall was compromised, the application of pressure quickly generated intravascular bubbles, with greater volumes and increased chaotic behavior appearing in larger channels with wider perforations, respectively. This reveals greater risks associated with gas embolism for patients who have weakened blood vessels, whether due to genetic factors or medical histories.The research also explores the impact of different anatomical and hematological factors on bubble behavior by varying the equivalent hematocrit concentrations of a synthetic blood solution in another designed series of microfluidic devices, comprising two channel widths and three wall thicknesses. These biological factors were correlated with the nucleation density, the generation frequency, the maximum volume, and the mean velocity of generated bubbles. The risks of bubble immobilization and vascular wall damage were determined as well, particularly in smaller channel widths, which were more susceptible to ruptures at lower pressure thresholds.Finally, a novel microscale hyperbaric system was created to model decompression sickness, using compressed air and nitrogen at different pressure thresholds and periods of compression. Rapid decompression from higher pressures and longer compression durations led to the formation of larger and more numerous bubbles, most of which appeared 10 minutes and even 30 minutes post-return to atmospheric conditions. This emphasizes the need for improved safety measures in scenarios involving pressure changes, especially since the symptoms of decompression sickness continue manifesting even 30 minutes post-decompression.These findings reveal the complex interplay of physical, biological, and environmental factors in gas embolism, highlighting the importance of personalized approaches in its management and prevention, as well as the development of stringent safety protocols for medical procedures involving gas introduction
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».